loprice.blogg.se

The equation of regression line is represented as: (i.e a value of x not present in a dataset) Now, the task is to find a line that fits best in the above scatter plot so that we can predict the response for any new feature values. Y as response vector, i.e y = įor n observations (in above example, n=10).Ī scatter plot of the above dataset looks like:. Let us consider a dataset where we have a value of response y for every feature x: Hence, we try to find a linear function that predicts the response value(y) as accurately as possible as a function of the feature or independent variable(x). It is assumed that the two variables are linearly related. Simple linear regression is an approach for predicting a response using a single feature. In order to provide a basic understanding of linear regression, we start with the most basic version of linear regression, i.e. Note: In this article, we refer to dependent variables as responses and independent variables as features for simplicity. Decision Tree Introduction with example.Removing stop words with NLTK in Python.Regression and Classification | Supervised Machine Learning.Basic Concept of Classification (Data Mining).Gradient Descent algorithm and its variants.ML | Momentum-based Gradient Optimizer introduction.Optimization techniques for Gradient Descent.

ML | Mini-Batch Gradient Descent with Python.Difference between Batch Gradient Descent and Stochastic Gradient Descent.Difference between Gradient descent and Normal equation.ML | Normal Equation in Linear Regression.Mathematical explanation for Linear Regression working.Linear Regression (Python Implementation).ISRO CS Syllabus for Scientist/Engineer Exam.